The disclosure relates to the field of predictive analytics generally, and particularly, to an efficient and scalable predictive analytics system based on sketching structured matrices in nonlinear regression problems.
A basic statistical problem that arises pervasively in data analytics applications is non-linear regression. On big datasets, solving such problems is computationally very challenging.
While development of randomized techniques for solving linear least squares regression and least absolute deviation problems exist, they do not exploit structure that is commonly found in regression problems. For example, in the polynomial fitting problem and certain kernel regression problems, the design matrix is heavily structured, which conceivably could lead to faster methods for solving the regression problems.
What is needed is a method to exploit this structure.